TAILIEUCHUNG - Cash flow prediction using artificial neural network and GA-EDA optimization

In this paper, 996 airplane maintenance basis data are used as a database, and 119 similar data are chosen after clustering. The project is divided into 20 equal periods and first three periods are used for simulating the next point. | Cash flow prediction using artificial neural network and GA-EDA optimization Journal of Project Management 4 2019 43 56 Contents lists available at GrowingScience Journal of Project Management homepage Cash flow prediction using artificial neural network and GA-EDA optimization Mohsen Sadegh Amalnika Hossein Iranmanesha Atabak Asgharia Ali Mollajana Vahed Fa- dakarb and Reza Daneshazarianc a Department of Industrial Engineering College of Engineering University of Tehran Tehran Iran b Faculty of Electrical Engineering Iran University of Science and Technology Tehran Iran c Renewable Energy Department Faculty of New Sciences and Technologies University of Tehran Tehran Iran CHRONICLE ABSTRACT Article history Cash flow models are one of the spotlights for evaluating a project. The actual data should be Received January 10 2018 modeled then it could be used for the prediction process. In this paper 996 airplane maintenance Received in revised format April basis data are used as a database and 119 similar data are chosen after clustering. The project is 1 2018 divided into 20 equal periods and first three periods are used for simulating the next point. The Accepted June 8 2018 Available online predicted data for each point is achieved by using of previous points from the beginning. The June 9 2018 model is based on artificial neural network and it is trained by three algorithms which are Ge- Keywords netic Algorithm GA Estimation of Distribution Algorithm EDA and hybrid GA-EDA Cash flow method. Two dynamic ratios are used which are dividing the population into two halves and the Neural network other is a ratio without dividing. The ratio would give a proportion to GA and EDA models in Genetic algorithm the hybrid algorithm and then the hybrid algorithm could model the system more accurately. Estimation of distribution algo- For each algorithm three main errors are calculated which are mean absolute percentage error rithm MAPE mean square .

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